Business analysts often find themselves in complicated situations where they are subject to demands from all directions. For them, responding to the needs of users and businesses requires a deep and analytical understanding. Many times, there is inadequate time allotted for BA work, and at other times, business analysts have requirements that IT can’t fulfill. Essentially, in all these scenarios, business analysts are tasked to concurrently manage fragile and error-prone analytics production pipelines and deliver value quickly.
The usage of data tables from IT and other data sources in analytics involves a significant amount of repetitive, manual labor. Firefighting and pressure are major business analytics challenges. DataOps serves as a means to use automation to standardize analytical operations rather than slapping people and money at issues.
The workflows that support the production of analytics are curated and managed by DataOps, which combines processes and workflows into a single process center. A DataOps process center gives corporate analytics teams a method to handle hurried demands & complex business analytics challenges without hiring more people or compromising on quality.
Today, the emergence of big data, the possibility of IoT, and breakthroughs in analytics are pushing data analysts to look for new ways to acquire and analyze data. To better understand DataOps and how it enhances data analysis to handle business analytics challenges, let’s first define it.
Table of Contents
What is DataOps?
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DataOps is an approach that integrates technology, procedures, concepts, and employees to automate data orchestration across a company. It offers a flexible data framework to deliver the correct data, at the right time, to the right stakeholders by uniting agile development, DevOps, employees, and the right data management technologies.
Using DataOps, every employee, from data analysts to marketers to salesmen, can use data to generate positive business outcomes. Today’s poorly resourced data and BI teams are, however, unable to keep up with these increasing requirements. DataOps can help in this situation.
DataOps expedites the creation and implementation of automated data workflows to provide high-quality, on-demand data to corporate BI teams.
Now, let’s understand DataOps in the context of analytics:
It’s an approach that includes the use of modern integration technology, procedures for converting raw data into a usable form, and data-related teams. The objective is to align both components of the data delivery equation. To ultimately maximize value for the company, the data manager must balance the needs of the business user for real-time, analytics-ready data with their own requirements for control, transparency, and auditability.
What is the Importance of DataOps?
The goal of DataOps is to develop data workflows that are flexible, scalable and manageable.
DataOps provides data consumers, internal and external stakeholders, and clients with the relevant data at the appropriate time. In the data economy, this gives businesses various competitive advantages, including:
1. Democratization of Data
DataOps unleashes data for employees throughout the whole firm, including CEOs, SDRs, and warehouse data users. DataOps improves performance, ROI, and competitiveness throughout the enterprise by nourishing every area with data.
2. Quicker Access to Data and Insights
DataOps provides key information to stakeholders more quickly, enabling stakeholders and teams to respond more quickly in a market that is dynamic.
3. Implement Decisive Action
With the proper information in everyone’s hands thanks to DataOps, decisions are made at every level of the business more effectively and with greater awareness.
4. Enhanced Data Performance
DataOps’ agile methodology facilitates data professionals to perform rapid, specific data pipeline deployments and modifications, reducing manual and time-consuming processes. Eliminating the need to wait for data to finish operations, it increases productivity for the data team as well as business data users.
5. Better Data Insights, Better Results
The DataOps methodology incorporates user feedback into pipeline development, resulting in the tailored insights that stakeholders require to boost revenue.
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Framework of DataOps
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A data architecture that supports continuous innovation, and collaboration among the data and engineering teams is an essential part of the DataOps framework. Members of the DataOps team create data architecture in different, almost similar scenarios and, following a predetermined testing procedure, send changes live using point-and-click capabilities.
Similar to DevOps, DataOps depends on automation to replace human processes and IT duties, such as:
- From intake to transformation to delivery, the complete data workflow is automated through data orchestration
- Automatic source code synchronization with the main repository
- With single-click deployments, data pipelines are launched into production
Business Analytics Challenges
Business analytics teams have continual deliverables such as a dashboard, a PowerPoint presentation, or a model that they want to update and renew. The demand to perform for business analysts is relentless.
As changes in the business environment occur, business analysts must promptly update their analytics production deliverables. No matter how much work is needed, customers and market circumstances determine deadlines and timeframes for analytics deliveries.
Here are some of the key business analytics challenges:
- BA teams frequently do not get a data platform that is suited to their requirements. Several unformatted tables, flat files, and other data are sent to them. They must assemble the 10 tables that are relevant to a given issue out of maybe hundreds of tables. Although tools like Alteryx might be helpful, business analysts still have a lot of manual labor to do.
- Business analyst teams frequently struggle with a constant sense of failure while wanting to service their internal customers more effectively and quickly. Although an analyst may put a lot of effort into providing their client with unique insights, it’s typical for a business associate to respond to analytics with a number of follow-up inquiries.
Furthermore, a productive analytics output only leads to additional inquiries and tasks. Consider a career where a “job well done” leads to an exponential increase in work.
- Sales and marketing personnel constantly bombard business analytical teams with queries, yet they are unable to advance. To respond to a variety of circumstances with the least amount of keystrokes, they want high-quality data in an answer-ready manner.
What they are actually receiving from IT and other data sources is subpar data in a format that needs to be manually customized. The activities they are completing, which take up 80% of their time, are vital but far apart from the insight and creativity that their business clients want.
How does DataOps Help Deal with Business Analytics Challenges?
DataOps offers a framework and technique to comprehend and handle these business analytics challenges. People are increasingly referring to data lake or data warehouse trends as data enablement or a data hub. DataOps takes this concept a step further by concentrating on the procedures and operations that allow data enablement and business analytics.
In essence, a DataOps process automates any data hub-related workflow that is feasible. A comprehensive procedure is specifically developed to help the data team to reduce the amount of time spent maintaining operational analytics and to update and improve current analytics with the least amount of labor.
All ad-hoc workflows are recorded as code by the DataOps process. Continuous testing is done to confirm and validate the information moving through the data pipelines. Before deploying new analytics, a set of impact reviews is run. Production analytics are revision controlled, and new analytics are effortlessly tested and deployed.
DataOps ensure all of the intellectual property associated with business analytics is kept (in Domino models, SQL, reports, tests, scripts, etc.) and can be shared, used again, reviewed, and continually enhanced even when employees change positions within the data organization.
Let’s sum this up in a simpler manner in terms of the benefits:
- Significantly Quicker Time-to-Insight for Ad-hoc Prompts – Gives the business analytics team access to a data representation for ad hoc queries that they can easily alter and control, which gives them more instant insight.
- Less Maintenance & Rework – With automation, the team can redirect time that was previously invested on business analytics challenges such as technical mechanics & rework to other beneficial duties, like answering consumer queries.
- Business Analytics Enablement – The DataOps’ primary goal is to supplement, not replace, a data hub (i.e., infrastructure built by IT, such as a data lake or warehouse). In order to meet the unique and changing demands of the business analytics team, the process modifies the standard data hub.
DataOps Roles & Responsibilities to Tackle Business Analytics Challenges
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Let’s now understand the roles and responsibilities in a BA team to effectively implement the DataOps process to solve business analytics challenges.
- Business Analyst/Data Scientist: Regardless of the title, this individual uses data to provide original insights for clients inside the business unit. This individual is tasked with explaining data in a manner that their non-technical, non-analytical business counterparts can understand. The final result can be a dashboard, model, graph, or chart. The DataOps enable this function, which often already exists, to continue to be actively invested in the value-added components of dealing with data.
- Analytical Engineer: This “data doer” serves as a link between what IT can provide and what business analytics wants. They provide the representations of data that business analysts require to execute their responsibilities using the data in an IT data hub. The analytics engineer’s productivity will only increase a little if this work is done manually. However, with the assistance of a DataOps engineer, the analytics engineer’s work improves business analyst productivity by an enormous margin.
- DataOps Engineer: The DataOps engineer develops the technological infrastructure, including automated processes and testing, so that the analytics engineer and business analyst may focus on their clients. The DataOps engineer builds and oversees the DataOps process, which renders the hidden visible.
The DataOps process facilitates the development of scalable, quick, iterative work. It may also be seen as the straightforward automation of all lifecycle processes and workflows from start to finish, including production orchestration, production data monitoring, and testing.
Final Thoughts
Business analysts are responsible for developing actionable insights. Since they share space with business clients, they understand their goals and difficulties. They make sure that the organization’s goods are consistently used, adopted, and recognized in order to satisfy customer needs.
The business analyst is directly involved in how the business unit uses data. They are the first to know if something goes wrong with data analytics. If they see the production flaws, broken reports, erroneous dashboards, and data inaccuracies, they experience tension, firefighting, and bravery firsthand.
DataOps is primarily an ideology and approach of “business-processes-as-code.” A DataOps methodology prevents you from continually adding employees to address issues. Automation is used to increase the agility and responsiveness of BA teams to the frequent demands from business units. Managers of BA teams can supervise a “well-oiled machine” of analysts iterating effectively on analytics to tackle rapidly evolving business analytics challenges with the help of a DataOps approach.
Give us your views on the business analytics challenges you encounter in your work routine!
Akshaan is a data science enthusiast who loves to embrace challenges associated with maintaining and exploiting growing data stores. He has a flair for writing in-depth articles on data science where he incorporates his experience in hands-on training and guided participation in effective data management tasks.